System and method for property detection and analysis

ABSTRACT

In variants, the method can include: detecting a property within a set of measurements; determining a set of property parameters based on the detection; determining a set of higher-resolution measurements based on the set of property parameters; and determining a set of property attributes based on the set of higher-resolution measurements.

CROSS REFERENCE TO RELATED APPLICATIONS

This application claims priority to U.S. Application No. 63/300,736filed 19 Jan. 2022, which is incorporated herein in its entirety by thisreference.

TECHNICAL FIELD

This invention relates generally to the property identification field,and more specifically to a new and useful method and system for propertystate tracking.

BACKGROUND

Determining reliable property data from images sampled over time can bechallenging for several reasons. First, the appearance of a givenproperty changes frequently between different image captures, eventhough the property itself has not changed. For example, different sunpositions in the sky can result in different shadow sizes, shapes, andorientations; vegetation coverage can vary drastically across seasons(e.g., a tree can seasonally obscure parts of the property); anddifferent imaging platforms can have different image spectral propertiesand image resolutions. Since property measurements are sparse andoftentimes need to be aggregated from different sources or across time,these disparities can result in inaccurate data for a given property,since it is difficult to determine whether a detected visual change isdue to data disparity or a change in the underlying property. Second,the property itself can change. For example, the property can bemodified (e.g., completion of property extension), removed (e.g.,demolition), added, replaced (e.g., replacing the property with adifferent property), or added over time (e.g., actively underconstruction). Such structural changes in the property can renderpreviously-determined property data invalid (since the property itselfhas since changed). Unfortunately, conventional imagery-based methodscannot reliably disambiguate whether divergent property data extractedfor a given property was due to changes in the property itself, errorsin the measurement (e.g., georeferencing errors), land movements,challenging sampling conditions (e.g., shadows, occlusions, etc.), orother reasons. Furthermore, auxiliary information that could be used todisambiguate these changes, such as permit data, are sparse, unreliable,and oftentimes unavailable.

Thus, there is a need in the property identification field to create anew and useful system and method for property state tracking.

BRIEF DESCRIPTION OF THE FIGURES

FIG. 1 is a flowchart representation of the method.

FIG. 2 is an illustrative example of a graph.

FIG. 3 is an illustrative example of determining polygons for astructure.

FIG. 4 is an illustrative example of a variant of the method.

FIG. 5 is an illustrative example of determining a set ofrepresentations.

FIG. 6A is an illustrative example of a graph.

FIG. 6B is a second illustrative example of a graph.

FIG. 7 is a schematic representation of determining relationshipsbetween representations.

FIG. 8 is an illustrative example of change detection.

FIG. 9 is an illustrative example of classifying the change betweenimage pairs.

FIGS. 10A-10E are illustrative examples of determining relationshipsbetween representations.

FIG. 11 is an illustrative example of relationships determined betweendifferent modality measurements for a given object.

FIG. 12 and FIG. 13 are schematic representations of a first and secondvariant of the method, respectively.

FIG. 14 is an illustrative example of an example of the method,including returning object version information responsive to a request.

FIG. 15 is an illustrative example of using an object detector.

DETAILED DESCRIPTION

The following description of embodiments of the invention is notintended to limit the invention to these embodiments, but rather toenable any person skilled in the art to make and use this invention.

1. Overview

As shown in FIG. 1 , variants of the method for property identificationanalysis can include: determining object information S100, determining aset of object representations S200, determining relationships betweenrepresentations S300, and generating an analysis based on therelationships S400. The method can function to disambiguate betweendifferent structural versions of a building and provide a universalidentifier for each building version. The method can further provide atimeseries of changes between different building versions.

2. Examples

In an illustrative example, the method can include: determining atimeseries of measurements of a geographic region (e.g., S100); anddetermining a timeseries of structure versions based on the timeseriesof measurements (e.g., S200 and S300), wherein different structureversions can be identified by different structure identifiers (e.g.,building identifiers). Examples of the illustrative example are shown inat least FIG. 6A, FIG. 6B, FIG. 12 , and FIG. 13 . The measurements canbe aerial images, depth measurements, and/or other measurements. Thegeographic region can be a property parcel, a prior structure version'sgeofence or mask, and/or other region. The timeseries of structureversions can be determined by: extracting a structure representation fora physical structure (e.g., a roof vector, a roof segment, a roofgeometry, a building vector, a building segment, etc.) from eachmeasurement of the timeseries; and associating structure representationsrepresenting the same structure version with a common structure version(e.g., the same structure identifier). Structure representationsrepresenting the same structure version can have the same representationvalues, have the same structural segments (e.g., physical segments,image segments, geometric segments, etc.), share structural segments,and/or otherwise represent the same structure version. Relationshipsbetween different structure versions can also be determined, based onthe respective structure representations (e.g., using rules, aclassifier, etc.). For example, a modified structure version can have anoverlapping set of structural segments with a prior structure version; areplaced structure version can have a disjoint set of structuralsegments with a prior structure version; an added structure version canhave a new set of structural segments when a prior structure version hadnone; and a removed structure version can have no structural segmentswhen a prior structure version had structure segments. Auxiliaryinformation for each structure version—such as measurements, permitdata, descriptions, attributes (e.g., determined from the measurements),and/or other information—can also be associated with the respectivestructure version (e.g., with the structure identifier) using a set ofspatiotemporal rules, or be otherwise associated. In variants, thetimeseries of building versions and associated relationships can berepresented by a graph (e.g., spatiotemporal graph), where the graphnodes can represent building versions and the edges can represent therelationships.

In use, information associated with a given structure version can bereturned responsive to a request identifying the structure version.Information can include: a set of changes (e.g., relationships) betweenthe identified structure version and another structure version (e.g., acurrent structure version), information from the structure version'stimeframe (e.g., auxiliary information, information extracted frommeasurements of the geographic region sampled during the timeframe,structure segments, etc.), and/or other information.

However, the method can be otherwise performed.

3. Technical Advantages

Variants of the technology for property identification analysis canconfer several benefits over conventional systems and benefits.

First, variants of the technology can associate information for aphysical object variant (e.g., object instance) with a singular,universal object identifier. This can enable the system to track andreturn object information for different variants of the “same” object,and can enable the system to identify when the physical object haschanged. For example, the system can automatically distinguish betweenand treat the original and remodeled version of a property as twodifferent property variants, and associate information for each propertyversion with the respective property version.

Second, variants of the technology can enable information from differentmeasurement modalities, different measurement vendors, and/or differentmeasurement times to be merged with an object identifier for a single(physical) object instance.

Third, variants of the technology can extract object representations(e.g., structure representations) that are agnostic to vendordifferences, common changes (e.g., occlusions; shadows; roof appearance(e.g., additional/removal of solar panels, roof covering replacement;etc.), measurement registration errors or changes (e.g., due to seismicshift), and other inter-measurement differences, which humans can havedifficulty distinguishing. For example, the technology can use a neuralnetwork model that is trained using self-supervised learning to generatethe same representation for the same physical property instance, despiteslight changes in the visual appearance of the property represented inthe measurements (e.g., the model is agnostic to appearance-basedchanges, but is sensitive to geometric changes and/or structuralchanges).

Fourth, variants of the technology can use the object representations todetermine the type of change. For example, the technology can use aneural network model that is trained to identify roof facets from themeasurement. A first and second object structure detected in a first andsecond measurement, respectively, can be considered the same buildingwhen the first and second measurement share roof facets.

Fifth, variants of the technology can minimize the number of measurementpairs to analyze, thereby increasing the computational efficiency of themethod, by limiting the object representation comparisons to thoselocated within and/or overlapping a common geographic region (e.g.,property parcel). For example, objects from different measurements ofthe same property parcel or geographic region can be assumed to have ahigh probability of being the same, so representation comparisons can belimited to those of objects extracted from measurements of the sameproperty parcel (or geographic region).

However, the technology can confer any other suitable benefits.

4. Method

The method can include: determining object information S100, determininga set of object representations S200, determining relationships betweenobject representations S300, and optionally generating an analysis basedon the relationships S400. However, the method can additionally and/oralternatively include any other suitable elements.

One or more variations of the system and/or method can omit one or moreof the above elements and/or include a plurality of one or more of theabove elements in any suitable order or arrangement. One or moreinstances of the method can be repeated for different properties,different timeframes, and/or otherwise repeated.

The method functions to associate information (e.g., measurements,representations, extracted features, auxiliary information, etc.) forthe same physical object version with a common object identifier. Themethod can optionally function to segregate information for differentobject versions. In variants, the method can also determine a timeseriesof object versions for the objects within a given geographic region.

The objects are preferably physical objects, but can be any otherobject. The objects can be: structures (e.g., built structures, such asbuildings, etc.), a portion of a structure (e.g., a building component,a roof, a wall, etc.), vegetation, manmade artifacts (e.g., pavement,driveways, roads, lakes, pools, etc.), and/or be any other suitablephysical object. The objects are preferably static (e.g., immobilerelative to the ground or other mounting surface), but can alternativelybe partially or entirely mobile.

Object versions 40 (e.g., examples shown in FIG. 4 and FIG. 12 ) arepreferably distinguished by different physical geometries (e.g.,internal geometries, external geometries, etc.), but can alternativelybe distinguished by different appearances and/or otherwisedistinguished. For example, different building versions can havedifferent sets of building segments (e.g., different roof segments),different building material (e.g., tile vs shingle roofing), and/or beotherwise distinguished. Conversely, the same building version can havethe same set of building segments, building material, and/or otherparameters.

Each object version 40 can be identified by a singular, universal objectidentifier. Alternatively, each object version can be identified bymultiple object identifiers. Each object identifier preferablyidentifies a single object version, but can alternatively identifymultiple object versions (e.g., identify an object geographic region,wherein different object versions can be distinguished by differenttimeframes). The object identifier can be a sequence of characters, avector, a token, a mask, and/or any other suitable object identifier.The object identifier can include or be derived from: the geographicregion (e.g., an address, lot number, parcel number, geolocation,geofence, etc.), a time stamp (e.g., of the first known instance of theobject version), a timeframe (e.g., of the object version), the objectrepresentation associated with the object version (e.g., a hash of theobject representation, the object representation itself, etc.), an index(e.g., version number for the geographic region), a random set ofalphanumeric characters (e.g., a set of random words, etc.), an objectversion within a series of related object versions, and/or any othersuitable information. For example, the object identifier can include orbe determined from a parcel number or address and a timestamp of thefirst measurement depicting the respective object version.

The method can be performed: in response to a request (e.g., an APIrequest) from an endpoint, before receipt of a request, when newmeasurements are received, at a predetermined frequency, and/or anyother suitable time. The method can be performed once, iteratively,responsive to occurrence of a predetermined event, and/or at any othertime. In a first variant, an instance of the method is performed usingall available object information (e.g., measurements) for a givengeographic region 10, wherein object representations are extracted fromeach piece of available object information and compared against eachother to distinguish object versions. In a second variant, an instanceof the method is performed using a specific set of object information(e.g., new object information, object information from a certaintimeframe, etc.), wherein the object representation is extracted fromthe set of object information and compared against other objectrepresentations for the geographic region 10 that were determined usingprior instances of the method to determine whether the objectrepresentation represents a known object version or a new objectversion. However, the method can be performed at any time, using anyother suitable set of object information.

The method can be performed for all objects within an object set (e.g.,all properties appearing in a map, within a geographic region, within alarge-scale measurement, etc.), a single object (e.g., a requestedbuilding, a requested parcel), a geographic region, a timeframe, and/orany other suitable set of objects.

The method can be performed for one or more geographic regions 10 (e.g.,examples shown in FIG. 3 , FIG. 6A, and FIG. 6B), wherein objectrepresentations for objects within and/or encompassing the geographicregion 10 are extracted and analyzed from object information depictingor associated with the geographic region to determine whether the objecthas changed over time. The geographic region 10 can be: a real estateparcel, the geographic footprint of an object version detected in adifferent piece of object information (e.g., measurement), a regionsurrounding the geographic footprint, a municipal region (e.g.,neighborhood, zip code, city, state, etc.), a geographic region depictedwithin a measurement, and/or any other suitable geographic region. In afirst variant, the geographic region includes the geofence for adetected or known object. In a second variant, the geographic regionincludes the geolocations within the geofence. In a third variant, thegeographic region includes a property parcel. In this variant, theanalyzed object representations can be limited to those for an object ofinterest (e.g., having a predetermined object class). In a fourthvariant, the geographic region includes a large region (e.g., propertyparcel, neighborhood, municipal district, etc.). In this variant, objectrepresentations for a plurality of objects can be extracted from objectinformation from different times, wherein temporally distinct objectrepresentations that are associated with overlapping geographicsubregions are analyzed (e.g., compared). However, any other suitablegeographic region can be used.

The method is preferably performed by a remote system (e.g., platform,cloud platform, etc.), but can additionally and/or alternatively beperformed by a local system or be performed by any other suitablesystem. The remote system can include a set of processing systems (e.g.,configured to execute all or portions of the method, the models, etc.),storage (e.g., configured to store the object representations, objectversions, data associated with the object versions, etc.), and/or anyother suitable component.

The method can be performed using one or more models. The model(s) caninclude: a neural network (e.g., CNN, DNN, encoder, etc.), a visualtransformer, a combination thereof, an object detector (e.g., classicalmethods, CNN based algorithms, such as Region-CNN, fast RCNN, fasterR-CNN, YOLO, SSD—Single Shot MultiBox Detector, R-FCN, etc.; feedforward networks, transformer networks, generative algorithms, diffusionmodels, GANs, etc.), a segmentation model (e.g., semantic segmentationmodel, instance-based semantic segmentation model, etc.), leverageregression, classification, rules, heuristics, equations (e.g., weightedequations), instance-based methods (e.g., nearest neighbor), decisiontrees, support vectors, geometric inpainting, Bayesian methods (e.g.,Naïve Bayes, Markov, etc.), kernel methods, statistical methods (e.g.,probability), deterministics, clustering, and/or include any othersuitable model or algorithm. Each model can determine (e.g., predict,infer, calculate, etc.) an output based on: one or more measurementsdepicting the object, tabular data (e.g., attribute values, auxiliarydata), other object information, and/or other information. The model(s)can be specific to an object class (e.g., roof, tree, pool), a sensingmodality (e.g., the model is specific to accepting an RGB satelliteimage as the input), and/or be otherwise specified. One model can beused to extract different representations of the set, but additionallyand/or alternatively multiple different models can be used to extractdifferent representations of the set.

Examples of models that can be used include one or more: objectrepresentation models, object detectors, object segmentation models,relationship models, and/or other models.

The object representation model functions to extract a representation ofthe object depicted or described within the object information. Theobject representation model preferably extracts the objectrepresentation from a single piece of object information (e.g., a singleimage, etc.), but can alternatively extract the object representationfrom multiple pieces of object information. The object representationmodel can include a single model (e.g., a CNN, an object detector, anobject segmentation model, etc.), a set of submodels (e.g., eachconfigured to extract a different attribute value from the objectinformation), and/or be otherwise configured.

In a first variant, the object representation model can be an objectsegmentation model that extracts a segment (e.g., semantic segment,instance-based segment, etc.) of an object of interest (e.g., objectclass of interest) from a measurement (e.g., an image, a DSM, etc.).

In a second variant, the object representation model can include a setof attribute models, each configured to extract a value for therespective attribute from the object information (e.g., contemporaneousobject information, same piece of object information, etc.). Examples ofattribute models can include those described in U.S. application Ser.No. 17/475,523 filed 15 Sep. 2021, U.S. application Ser. No. 17/526,769filed 15 Nov. 2021, U.S. application Ser. No. 17/749,385 filed 20 May2022, U.S. application Ser. No. 17/870,279 filed 21 Jul. 2022, U.S.application Ser. No. 17/858,422 filed 6 Jul. 2022, U.S. application Ser.No. 17/981,903 filed 7 Nov. 2022, U.S. application Ser. No. 17/968,662filed 18 Oct. 2022, U.S. application Ser. No. 17/841,981 filed 16 Jun.2022, and/or U.S. application Ser. No. 18/074,295 filed 2 Dec. 2022,each of which is incorporated herein in its entirety by this reference.However, other attribute models can be used.

In a third variant, the object representation model can be a model(e.g., CNN, encoder, autoencoder, etc.) trained to output the sameobject representation (e.g., feature vector) for the same object versiondespite common appearance changes in the object measurement (e.g., beagnostic to appearance-based changes but sensitive to geometricchanges). Common appearance changes can include: shadows, occlusions(e.g., vegetation cover, snow cover, cloud cover, aerial object cover,such as birds or planes, etc.), registration errors, and/or otherchanges. This object representation model preferably extracts the objectrepresentation from an appearance measurement (e.g., an image), but canalternatively extract the object representation from a geometricmeasurement (e.g., DSM, point cloud, mesh, etc.), geometricrepresentation (e.g., model), and/or from any other suitable objectinformation. In this variant, the object representation model can betrained to extract the same object representation (e.g., feature vector)for all instances of an object within a geographic region (e.g., thesame parcel, the same geofence, the object segment's geographicfootprint, etc.) from object information from different timeframes(e.g., images from different years, times of the year, etc.) (e.g.,example shown in FIG. 5 ). Even though the training data set mightinclude objects that have changed geometric configurations (e.g.,different object versions) and therefore should have resulted indifferent object representations, these changes are relatively rare, andin variants, do not negatively impact the accuracy of the model given alarge enough training set. Alternatively, this object representationmodel can be trained to predict the same object representation (e.g.,feature vector) using only object information that is known to describethe same object version.

In a fourth variant, the object representation model can be a featureextractor. The feature extractor can be: a set of upstream layers from aclassifier (e.g., trained to classify and/or detect the object classfrom the object information), an encoder, a classical method, and/or anyother suitable feature extractor. Examples of feature extractors and/ormethodologies that can be used include: corner detectors, SIFT, SURF,FAST, BRIEF, ORB, deep learning feature extraction techniques, and/orother feature extractors.

However, the object representation model can be otherwise configured.

The object detectors can detect the objects of interest (e.g., objectclasses of interest) within the object information (e.g., objectmeasurement, DSM, point cloud, image, wide-area image, etc.). Eachobject detector is preferably specific to a given object class (e.g.,roof, solar panel, driveway, chimney, HVAC system, etc.), but canalternatively detect multiple object classes. Example object detectorsthat can be used include: Viola Jones, Scale-invariant feature transform(SIFT), Histogram of oriented gradients (HOG), region proposals (e.g.,R-CNN), Single Shot MultiBox Detector (SSD), You Only Look Once (YOLO),Single-Shot Refinement Neural Network for Object Detection (RefineDet),Retina-Net, deformable convolutional networks, zero-shot objectdetectors, transformer-based networks (e.g., Detection Transformer(DETR), and/or any other suitable object detector.

An object detector can: detect the presence of an object of interest(e.g., an object class) within the measurement, determine a centroid ofthe object of interest, determine a count of the object of interestwithin the measurement, determine a bounding box surrounding the object(e.g., encompassing or touching the furthest extents of the object,aligned with or parallel the object axes, not aligned with the objectaxis, be a rotated bounding box, be a minimum rotated rectangle, etc.),determine an object segment (e.g., also function as a segmentationmodel), determine object parameters, determine a class for the object,and/or provide any other suitable output, given a measurement of theobject or geographic region (e.g., an image). Examples of objectparameters can include: the location of the object (e.g., location ofthe object within the image, the object's geolocation, etc.; from thebounding box location); the geographic extent of the object (e.g.,extent of the bounding box); the orientation of the object (e.g., withinthe image, within a global reference frame, relative to cardinaldirections, relative to another object, etc.; from the bounding boxorientation); the dimensions of the object (e.g., from the dimensions ofthe bounding box), and/or other parameters. The object detector canadditionally or alternatively be trained to output object geometries,time of day (e.g., based on shadow cues), and/or any other suitableinformation.

The object detector can be trained based on manual labels (e.g.,manually drawn bounding boxes), bounding boxes drawn around objectsegments (e.g., determined using a segmentation model from measurementsof the same resolution, higher resolution, or lower resolution),bounding boxes drawn around object geofences or masks (e.g., determinedfrom the locations of the object segments), and/or other trainingtargets. Alternatively, the object detector (e.g., a zero-shot objectdetector) can be generated using a zero-shot classification architectureadapted for object detection (e.g., by embedding both images and classlabels into a common vector space; by using multimodal semanticembeddings and fully supervised object detection; etc.), and/orotherwise generated. The detected object, parameters thereof (e.g.,location, orientation, bounding box size, object class, etc.), and/orderivatory entities (e.g., object segments) can be used to: retrievehigher-resolution measurements of the region (e.g., higher resolutionthan that used to detect the object; example shown in FIG. 15 );retrieve a different measurement modality for the region (e.g., depthmeasurements for the region); be used to determine the pose of theobject; be used to determine attribute values for the object; be used todetermine a geometric parameter of the object (e.g., the detectedbuilding shadow's length can be used to determine a building's height,given the building's geolocation and the measurement's sampling time);be used for multi-category multi-instance localization; determine countsand/or other population analyses; for time-series analyses (e.g., basedon objects detected from a timeseries of measurements); and/or beotherwise used. The higher-resolution measurements can encompass thedetected object's location, encompass the bounding box's geographicregion, encompass a smaller region than the wide-area measurement,and/or be otherwise related to the detected object. In variants, thehigher-resolution measurement can be used to extract a higher-accuracyobject segment using a segmentation model (e.g., a more accurate segmentthan that extractable from the original measurement; example shown inFIG. 15 ) and/or be otherwise used. In these variants, the bounding boxcan optionally be used to crop or mask the higher-resolution imagebefore object segmentation or not be used for segmentation. The objectsegment and/or object detection (e.g., bounding box; measurement portionwithin the bounding box, etc.) can then be used to determine objectattributes, feature vectors, and/or otherwise used. However, the objectdetector can be otherwise configured, and the outputs otherwise used.

The object segmentation models can determine the pixels corresponding toan object of interest (e.g., object class of interest). The objectsegmentation models can be preferably specific to a given object class(e.g., roof, solar panel, driveway, etc.), but can alternatively segmentmultiple object classes. The object segmentation model can be a semanticsegmentation model (e.g., label each pixel with an object class label orno object label), instance-based segmentation model (e.g., distinguishbetween different instances of the same object class within themeasurement), and/or be any other suitable model. The objectsegmentation model is preferably trained to determine objectrepresentations (e.g., masks, pixel labels, blob labels, etc.) fromobject measurements, but can alternatively determine the objectrepresentations from other object information. In variants, the objectmeasurements used by the object segmentation model can be higherresolution and/or depict a smaller geographic region than those used bythe object detector; alternatively, they can be the same, lowerresolution, and/or depict a larger geographic region.

The relationship model functions to determine the type of relationshipbetween two object versions and/or object representations. In a firstvariant, the relationship model can be a classifier (e.g., changeclassifier) trained to determine (e.g., predict, infer) a relationship(e.g., change) between two object variants and/or objectrepresentations, given the object representations. In a second variant,the relationship model can be a ruleset or set of heuristics. In anexample, the relationship model can associate object representationswith object versions based on a comparison of the geometric segmentsrepresented by the object representations. In a second example, therelationship model can associate information (e.g., auxiliaryinformation) with an object version when that information (e.g.,auxiliary information) is also associated with the same geographicregion and the same timeframe as the object version. In a third variant,the relationship model can be a similarity model configured to determinea distance between two object representations. However, the relationshipmodel can be otherwise configured.

However, any other suitable set of models can be used.

4.1 Determining Object Information S100.

Determining object information S100 functions to determine informationthat is representative of an object version.

The object information 20 (e.g., examples shown in FIG. 2 , FIG. 4 ,FIG. 12 , and FIG. 13 ) is preferably for a geographic region 10associated with an object of interest, but can additionally oralternatively be for an image segment, geometric segment, and/or anyother suitable entity. The geographic region associated with the objectof interest can be a parcel, lot, geofence (e.g., of a prior version ofthe object), neighborhood, geocode (e.g., associated with an address),or other geographic region.

In an illustrative example, S100 can retrieve imagery (e.g., aerialimagery) depicting the geographic region associated with an address(e.g., depicting the respective parcel, encompassing a geocodeassociated with the address, etc.). However, S100 can retrieve any othersuitable object information.

The determined object information can include one or more pieces ofobject information. For example, the object information can include: asingle measurement (e.g., image), multiple measurements, measurementsand attribute values, attribute values and descriptions, all availableobject information (e.g., for the geographic region, for the timeframe),and/or any other suitable set of object information.

The determined object information can be for one or more time windows(e.g., timeframes). The determined object information can include: allinformation associated with the geographic region, only new informationavailable since the last analysis of the object version and/or thegeographic region, the most recent information (e.g., for the geographicregion), only information within a predetermined time window (e.g.,within a request-specified time window or timeframe, from the lastanalysis until a current time, etc.), and/or any other suitable objectinformation. In variants, limiting the object information by time windowcan enable object representations to be determined for each time window,which enables the resultant object representations to be compared (andtracked) across different time windows. Alternatively or additionally,the determined object information can be unlimited by time.

The object information 20 can be determined: responsive to receipt of arequest (e.g., identifying the object identifier, identifying thegeographic region, etc.), when new information is available,periodically, at a predetermined time, and/or at any other suitabletime. In a first example, object information for a geographic region canbe retrieved in response to receipt of a request including an objectidentifier associated with the geographic region, wherein the retrievedobject information can be for the object version associated with theobject identifier and/or for other object versions associated with thegeographic region. In a second example, all object information for ageographic region can be periodically retrieved. However, the objectinformation can be determined at any other time.

The object information 20 can be from the same information provider(e.g., vendor), but can additionally and/or alternatively be fromdifferent information providers; example shown in FIG. 2 . Theinformation determined for different object versions preferably has thesame information modality (e.g., RGB, LIDAR, stereovision, radar, sonar,text, audio, etc.), but can additionally and/or alternatively havedifferent information modalities. The information can be associated withthe same information time (e.g., time the image was captured of theobject), but can additionally and/or alternatively be associated withdifferent information times. The information can be associated with thesame geographic region (e.g., parcel, lot, address, geofence, etc.), orbe associated with different geographic regions.

Each piece of object information 20 can be associated with geographicinformation (e.g., a geolocation, an address, a property identifier,etc.), a timeframe (e.g., a timestamp, a year, a month, a date, etc.),information provider, pose relative to the object or geographic region,and/or other metadata.

The object information 20 can include: measurements, models (e.g.,geometric models), descriptions (e.g., text descriptions, audiodescriptions, etc.), attribute values (e.g., determined from themeasurements or descriptions, etc.), auxiliary data, and/or otherinformation. The same type of object information is preferablydetermined for each object version and/or method instance;alternatively, different object information types can be determined.

Each measurement is preferably an image (e.g., RGB, NDVI, etc.), but canadditionally and/or alternatively be depth information (e.g., digitalelevation model (DEM), digital surface model (DSM), digital terrainmodel (DTM), etc.), polygons, point clouds (e.g., from LIDAR,stereoscopic analyses, correlated images sampled from different poses,etc.), radar, sonar, virtual models, audio, video, and/or any othersuitable measurement. The image can include tiles (e.g., of geographicregions), chips (e.g., depicting a parcel, depicting the portion of ageographic region associated with a built structure), parcel segments(e.g., of a property parcel), and/or any other suitable image segments.Each measurement can be geometrically corrected (e.g., orthophoto,orthophotograph, orthoimage, etc.) such that the scale is uniform (e.g.,image follows a map projection), but can alternatively not begeometrically corrected. Each measurement preferably depicts a top-downview of the object, but can additionally and/or alternatively depict anoblique view of the object, an orthographic view of the object, and/orany other suitable view of the object. Each measurement is preferablyremotely sensed (e.g., satellite imagery, aerial imagery, drone imagery,radar, sonar, LIDAR, seismography, etc.), but can additionally and/oralternatively be locally sensed or otherwise sensed. The measurementscan be of the object exterior, interior, and/or any other suitable viewof the property.

For example, measurements can be: wide-area images, parcel segments(e.g., image segment depicting a parcel), object segments (e.g., imagesegment depicting a structure of interest; example shown in FIG. 3 ),and/or any other suitable measurement. The image segments can be croppedto only include the entity of interest (e.g., parcel, structure, etc.),or include surrounding pixels.

The measurements of the set are preferably measurements of the samegeographic region (e.g., property, parcel, etc.), but can alternativelybe representative of different geographic regions. The property can be:a property parcel, an object, a point of interest, a geographic region,and/or any other suitable entity.

Additionally or alternatively, the measurements of the set can beassociated with (e.g., depict) the same physical object instance (e.g.,the tracked object instance) or different object instances. The objectcan include: a built structure (e.g., building, deck, pool, etc.), asegment of a built structure (e.g., roof, solar panel, door, etc.),feature of a built structure (e.g., shadow), vegetation, a landmark,and/or any other physical object.

The measurements and/or portions thereof can optionally be pre- orpost-processed. In a first example, a neural network model can betrained to identify an object of interest (e.g., an object segment)within a measurement (e.g., image, geometric measurement, etc.), whereinoccluded segments of the object of interest can be subsequently infilledby the same or different model. The occluded segments can be infilledusing the method disclosed in U.S. application Ser. No. 17/870,279 filed21 Jul. 2022, incorporated herein in its entirety by this reference,and/or using any other suitable method. In a second example, errantdepth information can be removed from a depth measurement. In anillustrative example, this can include removing depth information belowa ground height and/or removing depth information above a known objectheight, wherein the known object height can be obtained from buildingrecords, prior measurements, or other sources.

However, any other suitable object measurement can be determined.

The attribute values (e.g., property attribute values, object attributevalues, etc.) can represent an attribute of the object version. Examplesof object attributes can include: appearance descriptors (e.g., color,size, shape, texture, etc.), geometric descriptors (e.g., dimensions,slope, shape, complexity, surface area, etc.), condition descriptors(e.g., yard condition, roof condition, etc.), construction descriptors(e.g., roofing material, flooring material, siding material, etc.),record attributes (e.g., construction year, number of beds/baths,structure classification, etc.), classification attributes (e.g., objectclass, object type), and/or other descriptors. The object attributes canbe quantitative, qualitative, and/or otherwise valued. The objectattributes can be determined from object measurements, descriptions,auxiliary information, and/or other information. The object attributescan be determined: manually, using a trained model (e.g., trained topredict or infer the attribute value based on the object information),from a third party database, and/or otherwise determined. In examples,property attributes and/or values thereof can defined and/or determinedas disclosed in U.S. application Ser. No. 17/529,836 filed on 18 Nov.2021, U.S. application Ser. No. 17/475,523 filed 15 Sep. 2021, U.S.application Ser. No. 17/749,385 filed 20 May 2022, U.S. application Ser.No. 17/870,279 filed 21 Jul. 2022, and/or U.S. application Ser. No.17/858,422 filed 6 Jul. 2022, each of which is incorporated in itsentirety by this reference (e.g., wherein features and/or feature valuesdisclosed in the references can correspond to attributes and/orattribute values). The attribute values can be determined asynchronouslywith method execution, or be determined in real- or near-real time withrespect to the method.

Example attributes can include: structural attributes (e.g., for aprimary structure, accessory structure, neighboring structure, etc.),record attributes (e.g., number of bed/bath, construction year, squarefootage, legal class, legal subclass, geographic location, etc.),condition attributes (e.g., yard condition, yard debris, roof condition,pool condition, paved surface condition, etc.), semantic attributes(e.g., semantic descriptors), location (e.g., parcel centroid, structurecentroid, roof centroid, etc.), property type (e.g., single family,lease, vacant land, multifamily, duplex, etc.), property componentparameters (e.g., area, enclosure, presence, structure type, count,material, construction type, area condition, spacing, relative and/orglobal location, distance to another component or other reference point,density, geometric parameters, condition, complexity, etc.; for pools,porches, decks, patios, fencing, etc.), storage (e.g., presence of agarage, carport, etc.), permanent or semi-permanent improvements (e.g.,solar panel presence, count, type, arrangement, and/or other solar panelparameters; HVAC presence, count, footprint, type, location, and/orother parameters; etc.), temporary improvement parameters (e.g.,presence, area, location, etc. of trampolines, playsets, etc.), pavementparameters (e.g., paved area, percent illuminated, paved surfacecondition, etc.), foundation elevation, terrain parameters (e.g., parcelslope, surrounding terrain information, etc.), legal class (e.g.,residential, mixed-use, commercial), legal subclass (e.g., single-familyvs. multi-family, apartment vs. condominium), geographic location (e.g.,neighborhood, zip, etc.), population class (e.g., suburban, urban,rural, etc.), school district, orientation (e.g., side of street,cardinal direction, etc.), subjective attributes (e.g., curb appeal,viewshed, etc.), built structure values (e.g., roof slope, roof rating,roof condition, roof material, roof footprint, roof surface area,covering material, number of roof facets, roof type, roof geometry, roofsegments, etc.), auxiliary structures (e.g., a pool, a statue, ADU,etc.), risk scores (e.g., score indicating risk of flooding, hail, fire,wind, wildfire, etc.), neighboring property values (e.g., distance toneighbor, structure density, structure count, etc.), context (e.g.,hazard context, geographic context, weather context, terrain context,etc.), vegetation parameters (e.g., vegetation type, vegetation density,etc.), historical construction information, historical transactioninformation (e.g., list price, sale price, spread, transactionfrequency, transaction trends, etc.), semantic information, and/or anyother attribute.

Auxiliary data can include property descriptions, permit data, insuranceloss data, inspection data, appraisal data, broker price opinion data,property valuations, property attribute and/or component data (e.g.,values), and/or any other suitable data.

The object information can be: retrieved, extracted, and/or otherwisedetermined.

In a first variant, the set of measurements can be determined byretrieving the set of measurements from a database.

In a second variant, the set of measurements can be manually determined.

In a third variant, the set of measurements can be determined bysegmenting the set of measurements from a wide-area measurement (e.g.,representative of multiple properties). The set of measurements arepreferably determined based on parcel data, but can alternatively bedetermined independent of parcel data.

In a first example of the third variant, the set of measurements can besegmented from a wide-area measurement by determining a parcel segmentwithin the wide-area image based on a parcel boundary. In this example,object segments (e.g., representative of objects, roof, pool, etc.) canfurther be determined from the parcel segment using a segmentation model(e.g., semantic segmentation model, instance-based segmentation model,etc.).

In a second example of the third variant, the set of measurements can besegmented from a wide-area measurement by detecting objects within awide-area image (e.g., using an object detector). The object detectionscan optionally be assigned to parcels based on the object'sgeolocation(s).

In other examples of the third variant, the set of measurements can besegmented from a wide-area measurement as discussed in U.S. applicationSer. No. 17/336,134 filed 1 Jun. 2021, which is incorporated in itsentirety by this reference.

However, the set of measurements can be otherwise determined.

4.2 Determining a Set of Object Representations S200.

Determining a set of object representations S200 functions to determineobject representations for an object of interest or a geographic region.

The set of object representations 30 is preferably determined (e.g.,extracted) from the object information determined in S100, but canalternatively be determined from other information. The objectinformation can be of the same or different type (e.g., modality), fromthe same or different vendor, from the same or different time window,from the same or different geographic region (e.g., wherein thedifferent geographic regions overlap or are disjoint), and/or beotherwise related. The set of object representations are preferablydetermined from a set of measurements, but can additionally and/oralternatively be determined from a description, a set of attributevalues, and/or any other suitable object information.

One object representation 30 (e.g., a geometric representation) ispreferably determined from each piece of object information (e.g.,measurement) of the set, but alternatively multiple representations canbe determined from each piece of object information (e.g., differentrepresentation types for the same object, such as a geometricrepresentation and a visual representation for the same object;different representations for different objects depicted within the samemeasurement; etc.), one representation can be determined from multiplepieces of information (e.g., measurements) of the set, and/or any othersuitable set of representations can be determined from any suitablenumber of object information pieces (e.g., measurements).

The object representation 30 (e.g., representation) can represent: anobject class of interest within a geographic region, an object instanceof interest (e.g., an object version), and/or any other suitable object.The geographic region can be the same or different from that used inS100 to determine the object information. In a first example, S200 caninclude determining object representations for all buildings or roofsdepicted within a measurement (e.g., image or an image segment)depicting a given geographic region (e.g., a real estate parcel, aregion surrounding the geographic footprint of an object detected in aprior measurement, etc.). In a second example, S200 can includedetermining the object representation for a specific building or roofdepicted within the measurement. However, any other suitable set ofobject representations can be determined for a given object instance orgeographic region.

Examples of object representations 30 that can be determined caninclude: an appearance representation (e.g., representative of anobject's visual appearance), a geometric representation (e.g.,representative of an object's geometry), an attribute representation(e.g., representative of the object's attribute values), a combinationthereof, and/or any other suitable representation. Examples ofappearance representations can include: vectors (e.g., encoding valuesfor different visual features or appearance-based features), matrices,object segments (e.g., measurement segments depicting substantially onlythe object, image segments, masks, etc.), bounding boxes encompassingthe object, and/or other appearance-based representations. Examples ofgeometric representations can include: a geometric object segment (e.g.,segment of a DSM, segment of a point cloud, masks, etc.), a bounding box(e.g., bounding box, bounding volume, etc.) encompassing the object, amesh, vectors (e.g., encoding values for different geometric features),a set of geometric feature values (e.g., a set of planes, facets,points, edges, corners, peaks, valleys, etc.) and/or parameters thereof(e.g., count, position, orientation, dimensions, etc.), an array, amatrix, a map, polygons or measurement segments (e.g., example shown inFIG. 3 ), a model, and/or other geometric representations. Examples ofattribute representations can include a vector of values for one or moreattributes, and/or be otherwise represented. The attribute values for agiven attribute representation are preferably determined from the samepieces of object information (e.g., the same measurements), but canadditionally or alternatively be determined from contemporaneous objectinformation and/or other information.

The type of object representations that are determined in S200 can be:predetermined, determined based on the type of measurement (e.g.,geometric representations are extracted from Lidar, RGB images, and DSMdata, while appearance representations are extracted from RGB images),and/or otherwise determined. The same type of representation ispreferably determined for each time window for a given geographicregion, such that the representations can be compared against eachother; alternatively, different representation types can be determinedfor each time window for a given geographic region.

The representations 30 of the set are preferably representative of thesame object, but can additionally and/or alternatively be representativeof different objects. The representations can be for the same ordifferent object versions. The representations are preferablyunassociated with an object identifier when the representations areinitially determined (e.g., wherein the representation is associatedwith the object identifier in S300), but can alternatively be associatedwith an object identifier by default (e.g., associated with the lastobject identifier for the property by default, wherein S300 evaluateswhether the object identifier is the correct object identifier).

Object representations 30 can additionally and/or alternatively beassociated with the metadata for the object information, such as thetimestamp, vendor, geolocation, and/or other metadata. In variants whereobject representations are determined from a timeseries of objectinformation (e.g., measurements), S200 can generate a timeseries ofobject representations.

Object representations 30 can additionally and/or alternatively beassociated with: parcel information (e.g., parcel ID, parcel boundaries,parcel geolocation(s), associated addresses, etc.), attribute valuesextracted from or associated with the object information (e.g.,attribute values extracted from the object information, etc.), auxiliaryinformation (e.g., associated with the representation via the parcel,tax information, permit data, address information, square footage,number of beds and baths, etc.), and/or any other suitable auxiliarydata. The auxiliary data can be associated with a representation:spatially (e.g., wherein the auxiliary data is associated with ageographic location within the geographic region, with an address sharedwith the representation, etc.), temporally (e.g., wherein the auxiliarydata and the representation are associated with the same time window,etc.), spatiotemporally, and/or otherwise associated with arepresentation.

The object representation 30 is preferably determined by an objectrepresentation model, but can alternatively be determined by any othersuitable model, be determined by a user, be retrieved from a database,or be otherwise determined. The object representation can be: extracted,calculated, aggregated, and/or otherwise determined from the objectinformation.

In a first variant, a feature vector (e.g., appearance feature vector,geometric feature vector, etc.) can be extracted from the objectinformation using a neural network model (e.g., example shown in FIG. 5). The object information is preferably a measurement, more preferablyan image or a geometric measurement (e.g., DSM), but can alternativelybe any other suitable type of object information. In a specific example,the model can be trained to be invariant to common appearance changes(e.g., occlusions, shadows, car presence, registration errors, etc.),wherein the structure appearance of the property can be consideredunchanged when the pair of vectors substantially match (e.g., do notdiffer beyond a threshold difference). In a second embodiment, the modelcan be trained to not be invariant to common appearance changes.

In a second variant, the object representation is a measurement segmentdepicting the object. The object segment can be an image segment (e.g.,determined from an image), a geometric segment (e.g., determined from ageometric measurement), an object component (e.g., a roof facet, etc.),and/or be otherwise configured. The object segment can be georegisteredand/or unregistered. In a first example, an image segment can bedetermined using an object segmentation model trained to segment theobject class of interest. In an illustrative example, a roof segment canbe determined within an image (e.g., of a parcel, a wide-area image,etc.) using a roof segmentation model. In a second example, a geometricsegment can be determined by masking a geometric measurement using amask registered to the geometric measurement. The mask can be: an objectsegment or mask determined from an image (e.g., contemporaneously orconcurrently sampled with the geometric measurement), an object segmentfor an known object version, and/or be any other suitable mask. In athird example, the geometric measurement segment can be determined usingan object segmentation model trained to detect segments of an objectclass of interest within a geometric measurement. In an example, aneural network model (e.g., segmentation model) can be trained todetermine object instances within an image, such as by using the methodsas discussed in U.S. application Ser. No. 17/336,134 filed 1 Jun. 2021,which is incorporated in its entirety by this reference. However, themeasurement segment can be otherwise determined.

In a third variant, the object representation includes a set ofdimensions for the object, and can additionally or alternatively includea location and/or orientation for the object. For example, the geometricrepresentation can include a length and width of the object, and canoptionally include the height of the object. In this embodiment, thegeometric representation can be determined using an object detector(e.g., wherein the dimensions and other parameters, such as location ororientation are determined from the dimensions and parameters of thebounding box), an object segmentation model (e.g., wherein thedimensions and other parameters are determined from the object segment),and/or any other suitable object representation model.

In a fourth variant, the object representation includes a set ofcomponents of the object, and can optionally include the parameters foreach component and/or characteristics of the set. Examples of componentscan include: segments, planes, facets, edges (e.g., peaks, valleys,etc.), corners, and/or other object components. For example, the objectrepresentation can include a set of roof segments or roof facets for abuilding, and can optionally include the pose (e.g., location,orientation, surface normal orientation, etc.), dimensions, slope,and/or other parameter of each component, and/or include the count,distribution (e.g., size distribution, slope distribution, etc.) and/orother characteristic of the set. In a first embodiment, the componentsare determined from a geometric segment (e.g., determined using thefirst variant), wherein a set of planes (e.g., the components) arefitted to the geometric segment. The model or a second model canoptionally distinguish between adjacent components. For example, a modelcan identify disjoint planes by optionally corresponding planes to depthinformation and identify disjoint planes with different normal vectors(e.g., determined based on depth information, etc.) as differentcomponents (e.g., different roof segments). However, differentcomponents can be otherwise distinguished. In a second embodiment, thecomponents are determined using a model trained to determine componentinstances (e.g., roof segments) and/or extract the component parametersand/or characteristics from the object information (e.g., objectmeasurement). However, the component set, parameters thereof, and/orcharacteristics thereof can be otherwise determined.

In a fifth variant, the object representations include an attribute set(e.g., attribute vector) for the object. The attributes within theattribute set are preferably the same attributes for all objects (andobject versions), but can alternatively include different attributes.The attribute values within the attribute set are preferably determinedfrom the same piece of object information, but can additionally oralternatively be determined from contemporaneous object information(e.g., object information captured, generated, or otherwise associatedwith a common time frame, such as within 1 day, 1 week, 1 month, or 1year of each other), from object information known to be associated withthe same object version, and/or from any other suitable set of objectinformation. For example, the attribute vector can include values foreach of a set of attributes that were extracted from the samemeasurement (e.g., image) or from measurements sampled the same day(e.g., by the same vendor, by different vendors, etc.). The attributevalues can be determined by one or more attribute models, and/orotherwise determined.

In a sixth variant, the object representations can include a combinationof the above variants.

However, the set of representations can be otherwise determined.

4.3 Determining Relationships Between Representations S300.

Determining relationships between object representations S300 functionsto determine whether the object representations within the set areassociated with different physical object versions and/or how thephysical object versions are related with each other.

Different object versions 40 are preferably different versions of thesame object (e.g., an original building and a remodeled building aredifferent versions of the same building), but can additionally oralternatively be different objects altogether (e.g., an originalbuilding and a new building post demolition).

Different object versions 40 preferably have different physicalgeometries (e.g., different footprints, different boundaries, differentheights, different topography, etc.), but can additionally oralternatively have different appearances (e.g., different paint,different roofing or siding material, etc.), and/or be otherwisedifferentiated.

Different object versions 40 can be associated with different objectidentifiers. Each object identifier preferably identifies a singleobject version (e.g., example shown in FIG. 6B), but can alternativelyidentify multiple object versions (e.g., identify all related buildingslocated on the same or overlapping geographic region; example shown inFIG. 6A). The identifiers are preferably globally unique (e.g., areuniversally unique identifiers (UUID) or globally unique identifiers(GUID)), but can additionally and/or alternatively be locally unique(e.g., to the geographic region), not be unique, and/or otherwiserelated. The object identifier can be: a hash of the object'scharacteristics (e.g., geometric characteristics, visualcharacteristics, etc.), generated from the object representationsassociated with the object version, a randomly generated identifier, anindex, a nonce, be generated based on a geographic region identifier, begenerated based on the object version parameters (e.g., timeframe,etc.), be generated based on a prior object version's identifier, aunique combination of semantic words or alphanumeric characters, or beany other suitable identifier. The geographic region identifier can bean address, a hash of an address, an index, randomly generated, and/orotherwise determined. Examples of object identifiers (e.g., buildingidentifier, structure identifier, etc.) include: an address andtimestamp combination (e.g., wherein the timestamp is associated withthe object version), a hash of the object representation associated withthe object version, an object representation associated with the objectversion, and/or any other identifier.

The relationships 50 (e.g., examples shown in FIG. 7 , FIG. 12 , FIG. 13, etc.) are preferably determined by comparing the objectrepresentations 30, but can alternatively be manually determined,predicted (e.g., using a model, from the object representations 30 orthe underlying object information 20), and/or be otherwise determined.

The compared object representations are preferably associated with thesame geographic region (e.g., same parcel, the same geofence, the samegeolocation, the same address, etc.), but can additionally and/oralternatively be associated with different geographic regions (e.g.,disjoint geographic regions), related geographic regions (e.g., whereinone geographic region overlaps with the other), be randomly selected(e.g., from a set of representations extracted for a plurality of objectinstances), and/or be otherwise determined. The compared objectrepresentations are preferably temporally adjacent or sequential, butcan alternatively be temporally disparate (e.g., separated by one ormore intervening object representations). In a first example, allrepresentations associated with the same geographic region are comparedwith each other. In a second example, temporally adjacentrepresentations are compared with each other. However, any other set ofrepresentations can be compared.

S300 can include determining whether the object representations 30represent the same object version 40.

Object representations indicative of the same physical object geometries(and/or appearances) are preferably associated with the same objectversion, while object representations indicative of different physicalobject geometries (and/or appearances) are preferably associated withdifferent object versions. Alternatively, object representationsindicative of different physical object geometries and/or appearancescan be associated with the same object version, while objectrepresentations indicative of the same physical object geometries and/orappearances can be associated with different object versions.

Object representations that represent the same object version cansubstantially match (e.g., are an exact match or match within apredetermined margin of error, such as 0.1%, 1%, 5%, etc.), have asimilarity greater than a similarity threshold (e.g., Jaccard Index,Sorensen-Dice coefficient, Tanimoto coefficient, etc.), or have adistance less than a threshold distance (e.g., the cosine distance,Euclidean distance, Mahalanobis distance, or other similarity distanceis less than a threshold distance); alternatively, the objectrepresentations representative of the same object version can beotherwise determined. Object representations representative of the sameobject version are preferably sequential, serial, or temporally adjacent(e.g., there is no intervening object representation or object version),but can alternatively not be sequential. For example, an object versioncan be determined for each set of temporally adjacent objectrepresentations that are substantially similar. Object representationsrepresentative of different object versions are preferably substantiallydifferentiated (e.g., the differences exceed a margin of error) and/orhave less than a threshold similarity (e.g., the cosine distance,Euclidean distance, Mahalanobis distance, or other similarity distanceis higher than a threshold distance); alternatively, the objectrepresentations representative of different object versions can beotherwise determined.

In a first variant, determining whether the object representationsrepresent the same object version can include comparing the objectsegments. In a first embodiment, comparing the object segments includesdetermining the object segment overlap (e.g., geographic overlap),wherein the object representations represent the same object versionwhen the overlap exceeds a threshold and/or the non-overlapping portionsfalls below a threshold. In a second embodiment, comparing the objectsegments includes comparing the segment projections (e.g., footprints)and/or geometries, wherein the object representations represent the sameobject version when the projections and/or geometries are similar abovea threshold. This embodiment can mitigate against registration errorsbetween the underlying object measurements. However, the segments can beotherwise compared.

In a second variant, determining whether the object representationsrepresent the same object version can include comparing feature vectors(e.g., extracted using the appearance change-agnostic model) and/orattribute vectors. In this variant, the object representations can beconsidered to represent the same object version when the vectors match(e.g., exactly or above a threshold similarity). However, the featurevectors can be otherwise compared.

In a third variant, determining whether the object representationsrepresent the same object version can include comparing component setsand/or parameters or characteristics thereof. In a first embodiment, theobject representations represent the same object version when thecomponents within the sets match, or when one set is a subset of theother. In a second embodiment, the object representations represent thesame object version when the component sets have substantially the samecomponent parameter or characteristic distributions. For example, theobject representations represent the same object version when the numberof roof facets are substantially the same, when the slope distributionis substantially the same, when the roof peak or valley locations aresubstantially the same, and/or other component parameters orcharacteristics are substantially the same.

However, whether the object representations represent the same ordifferent object versions can be otherwise determined.

S300 can additionally or alternatively include determining whether theobject versions or object representations are related, and/or how theobject versions or object representations are related. Object versionscan be considered related when they are the same object version, arederivatory object versions (e.g., one object version is a modifiedversion of the other object version), or otherwise related. Objectrepresentations can be considered related when they represent the sameobject version, represent derivatory object versions (e.g., one objectversion is a modified version of the other object version), or otherwiserelated.

Relationships 50 can be characterized by: a relationship class (e.g.,relationship type), a similarity metric (e.g., cosine distance,affinity, etc.), an amount of change, the changed segments (e.g., wingadded/removed, highlight the portions of a building that have beenmodified, etc.), and/or otherwise characterized. Examples ofrelationship classes can include: same, unchanged, modified, added,removed, replaced, or other relationship types. Relationships 50 can bebetween object representations 30, object versions 40, and/or any othersuitable entity.

In a first illustrative example, two object versions are considered the“same” when the geometries (e.g., represented by object segments, objectcomponent sets, object feature sets, attribute vectors, etc.)substantially match; “modified” when a prior object version sharesgeometric segments with the latter object version (e.g., the geometricsegments intersect; the component sets intersect or include sharedcomponents); “replaced” when a prior object version does not sharegeometric segments with the latter object version (e.g., the geometricsegments are disjoint; the component sets do not share components);“added” when there was no prior object version (e.g., in the geographicregion) and there is a latter object version; and “removed” when therewas a prior object version (e.g., in the geographic region) and there isno latter object version.

In a second illustrative example, two object representations areconsidered to be the “same” when the respective object versions have thesame geometries; “modified” when the object version represented by theprior object representation (“prior object version”) shares geometricsegments with the object version represented by the latter objectrepresentation (“latter object version”); “replaced” when the priorobject version does not share geometric segments with the latter objectversion (e.g., the geometric segments are disjoint; the component setsdo not share components); “added” when the prior object representationrepresents no object (e.g., is indicative of an empty space) and thelatter object representation represents an object; and “removed” whenthe prior object representation represents an object and the latterobject representation represents no object (e.g., is indicative of anempty space).

However, the relationships can be otherwise defined.

A single relationship is preferably determined between each pair ofobject versions and/or object representations; alternatively, multiplerelationships can be determined between each pair of object versionsand/or object representations.

Collectively, the method (e.g., one or more instances of S300) candetermine a set of relationships tracking a history of object changesover time (e.g., a building change history). The set of relationshipscan be determined for: a geographic region (e.g., a parcel), an object,or for any other entity. In a first variant, the set of relationshipscan be specific to an object (e.g., a building, a tree, a pool), whereinthe representations connected by the set of relationships are associatedwith the same object, and describe the relationship between the objectversions (e.g., object states; how the object has changed over time). Ina second variant, the set of relationships can be specific to ageographic region (e.g., a parcel), wherein the representationsconnected by the set of relationships cooperatively describe howdifferent objects within the geographic region are related (e.g., therelationship between buildings on the property over time). However, theset of relationships can be otherwise specified.

This set of relationships 50 between the object versions 40 and/orobject representations 30 can cooperatively form a graph (examples shownin FIG. 6A and FIG. 6B), but can be represented as a matrix, a table, aset of clusters, and/or otherwise represented. The graph can be apartially connected graph, a fully connected graph, and/or any othersuitable type of graph.

In a first variant, nodes of the graph can represent object versions,and edges of the graph (e.g., connecting pairs of nodes) representrelationships between the object versions. The nodes (e.g., objectversions) can additionally or alternatively be associated with objectrepresentations, auxiliary data (e.g., measurement identifiers,timestamps, etc.), object information, and/or any other suitable data.

In a second variant, the nodes of the graph can be objectrepresentations. In this variant, the object representations associatedwith the nodes can include object representations extracted from allavailable data for a given property or object, only the latestrepresentation (e.g., to detect changes; be a 2-node graph), and/orrepresent any other set of data or timeframes. In this variant, objectrepresentations connected by a “same” relationship can be assigned thesame object version identifier, while object representations connectedby other relationships can be assigned different object versionidentifiers.

The nodes are preferably ordered by time, but can alternatively beordered by the relationship type, by statistical distance, or otherwiseordered. Connected nodes are preferably associated with the samegeographic region, but can alternatively be associated with the sameobject and/or be otherwise associated.

The edges are preferably associated with a relationship type (exampleshown in FIG. 6A and FIG. 6B), but can additionally and/or alternativelynot be associated with a relationship type. In a first variant, theedges can be defined between representations of the same building (e.g.,a relationship between the representations is classified as “unchanged”or “modified”); example shown in FIG. 6A and FIG. 6B. In a secondvariant, the edges can be defined between representations of differentbuildings (e.g., different buildings are associated with the sameproperty). However, the edges can be otherwise defined.

However, the set of relationships between object versions and/or objectrepresentations can be otherwise represented.

The relationship 50 between two object versions 40 is preferablydetermined based on the respective object representations 30 (e.g., theobject representations used to distinguish between the object versions,different object representations, etc.) (e.g., example shown in FIG. 12), but can additionally or alternatively be determined based on theobject information associated with the object versions (e.g., objectinformation generated during each object version's timeframe, objectinformation used to determine the object representations, etc.),auxiliary data associated with each object version, and/or any othersuitable information.

Object version relationships can be determined using a trained model, bedetermined using a ruleset, be calculated, be manually assigned, and/orbe otherwise determined (e.g., examples shown in FIG. 4 ).

In a first variant, S300 includes classifying the relationship betweenobject representations (and/or respective object versions); exampleshown in FIG. 7 . The classifier can ingest a set of objectrepresentations (e.g., a representation pair) and output a relationshipclassification (e.g., “unchanged”, “modified”, “replaced”, “added”,“removed”, etc.). The representations can be feature vectors (e.g.,determined using the appearance change-agnostic model), measurementsegments, attribute vectors, and/or other representations. Therepresentations are preferably the same representation type (e.g., bothare segments, both are feature vectors, both are attribute vectors,etc.), but can alternatively be different representation types. Theclassifier can be trained using ground-truth data including objectrepresentation sets (e.g., vector pairs, extracted from differentmeasurements) labeled with the respective relationship class (e.g.,change type labels), and/or using any other training data. Theground-truth relationship class can be determined manually, labeledusing contemporaneous permit or tax data (e.g., labeled as “modified”when a permit exists for the timeframe, labeled as “removed” when thetax records show no improvements/land only, labeled as “replaced” whenthe taxable value changes but no sale has occurred, etc.), but canalternatively be labeled using any other suitable method. For example, aset of appearance vectors extracted from different measurements by thesame model can be fed to the classifier, wherein the classifier predictsa relationship label for the appearance vector set.

In a second variant, S300 includes determining the relationship betweenthe object representations (and/or respective object versions) based ona comparison metric between the representations. In this variant, therepresentations can be a vector of attribute values (e.g., such as roofpitch, roof slope, etc.; example shown in FIG. 8 ), be a feature vector,and/or be any other representation. The attribute within the vector canbe selected to be indicative of object instance similarity (e.g., usingSHAP values, lift, etc.), be unselected (e.g., include all attributes),or be otherwise selected. The comparison metric can be a distance metric(e.g., Euclidean distance, Chebyshev distance, etc.), a similaritymetric (e.g., Jaccard similarity, cosine similarity, etc.), adissimilarity metric, and/or any other suitable comparison metric. In afirst embodiment, the distance metric values can be binned orthresholded, wherein each bin or threshold is associated with adifferent relationship class (e.g., small distances are binned to“unmodified” or “modified”, wherein large distances are binned to“added”, “removed”, or “heavily modified”, etc.). However, the distancescan be otherwise interpreted.

In a third variant, relationships between the representations (and/orrespective object versions) can be determined based on rules andheuristics; example shown in FIG. 9 . In this variant, therepresentations are preferably geometric representations, but can beother representations.

In a first example, each representation can include a set of rooffacets. The relationship between a pair of representations (and/orrespective object versions) can be considered: unchanged when the rooffacets match; modified when the geometric representations share at leastone roof facet; added when the prior geometric representation had noroof facets and the subsequent geometric representation has some rooffacets; removed when the prior geometric representation had some rooffacets and the subsequent geometric representation has no roof facets;and replaced when the pair of geometric representations both have rooffacets but none match; example shown in FIGS. 10A-10E. In this example,a structure can be considered the same structure when the subsequent andprior structures share a common roof facet or segment (e.g., when thestructure is unchanged or is modified). Otherwise, the structure can beconsidered a different structure (e.g., a different object variant). Ina second example, each representation can include a set of measurementsegments (e.g., image segments or geometric segments). The relationshipbetween a pair of representations (and/or respective object versions)can be considered: unchanged when the segments match; modified when thesegments overlap; added when there was no prior measurement segment andthere is a subsequent measurement segment; and removed when there was aprior measurement segment and there is not a subsequent measurementsegment.

In a second example, permits (e.g., remodeling permits) relevant to theobject (e.g., property) from the same timeframe as the pair ofrepresentations can indicate that an object was modified.

In a third example, tax information relevant to the property from thesame timeframe as the representations can signal that an object wasmodified. This auxiliary data (e.g., permit data, tax data, etc.) can beused to validate, disambiguate, specify, or otherwise influence arelationship between representations. However, rules, criteria, and/orheuristics can otherwise determine relationships between therepresentations.

In a fourth variant, relationships between the representations can bedetermined based on a cascade of analysis modules. In a first example, afirst analysis module can use geometric analysis to determine coarsechanges (e.g., added, removed, same footprint, different footprint,etc.) and a second analysis module can use appearance-based methodsand/or classifiers to distinguish between finer changes after the coarsechanges have been determined (e.g., detect occurrence of a remodel ifsame footprint, determine magnitude of change if different footprint,etc.). In a second example, a first analysis module can useappearance-based methods to determine whether the structure has changed,a second analysis module can use geometric representation (e.g.,extracted from the same measurement or a contemporaneous measurement) todetermine whether the structure remains the same, and a third analysismodule can use appearance representation to determine (e.g., classify)the type of structure change. In a third example, the second variant canbe used to determine the object representations that represent the sameobject versions (e.g., wherein vectors that are substantially similarrepresent the same object versions), then the first variant or the thirdvariant can be used to classify the relationships between the objectversions. However, the cascade of analysis modules can otherwisedetermine relationships between the representations.

However, the relationships can be otherwise determined.

S300 can additionally include identifying object versions 40 based onthe relationships. In a first variant, an object version can beassociated with or identified as a set of related object relationships(e.g., set of connected nodes); example shown in FIG. 6A. In a secondvariant, an object version can be associated with or identified asrepresentations related by a predetermined subset of relationship types(e.g., only unchanged or “same”, example shown in FIG. 6B; onlyunchanged and modified, etc.). In a first example, objectrepresentations representing the same object version are identified,then relationships between the object versions are determined (e.g.,example shown in FIG. 12 ). In a second example, relationships betweenthe object representations can be determined, wherein object versionscan be determined based on the relationships (e.g., using a set ofrules), and the relationships between different object versions can bedetermined from (e.g., inherited from) the relationships between therespective object representations (e.g., example shown in FIG. 13 ). Ina third variant, an object version can be associated with or identifiedas a set of substantially similar object representations. However, theobject version can be otherwise determined.

Each object version 40 can be assigned one or more object identifiers,as discussed above.

Each determined object version 40 can be associated with: a timeframe,the geographic region (e.g., from the object representations and/orobject information), the related object representations, the objectinformation used to determine the related object representations,auxiliary data spatially and/or temporally related to the objectversion, and/or other information.

The timeframe for an object version can encompass all of the relatedobject representations' timestamps (e.g., examples shown in FIG. 12 andFIG. 13 ) and/or exclude one or more related object representations'timestamps. The beginning of the timeframe for an object version can be:the timestamp of the first related object representation, the timestampof the last object representation associated with a prior objectversion, a manually-specified time, a time determined fromchange-related object information (e.g., permit information,construction information, etc.), and/or otherwise determined. The end ofthe timeframe for an object version can be: the timestamp of the lastobject representation related to the object version, the timestamp ofthe first object representation associated with a subsequent objectversion, a manually-specified time, a time determined fromcompletion-related object information (e.g., final certification,insurance documentation, etc.), and/or otherwise determined. However,the object version timeframe can be otherwise defined.

The auxiliary data associated with an object version can be associatedwith the object version: spatially (e.g., both are associated with thesame geographic region), temporally (e.g., the auxiliary data has atimestamp falling within the object version timeframe),spatiotemporally, by object information (e.g., both the objectrepresentation associated with the object version and the auxiliary datawere determined from the same object information), associated based onparameters (e.g., spatial parameter, temporal parameter, etc.) sharedwith the object version and/or the measurements associated with theobject version, and/or be otherwise associated with the object version.Auxiliary data can include: object information from other vendors,object representations determined using object information from othervendors, object descriptions (e.g., property descriptions), permit data,insurance data (e.g., loss data, assessment data, etc.), inspectiondata, appraisal data, broker price opinion data, property valuations,attribute and/or component data (e.g., values), and/or other data.

However, the object versions can be associated with any other suitableinformation.

4.4 Generating an Analysis Based on the Relationships S400.

Generating an analysis based on the relationships S400 functions todescribe an object version, the timeseries of object versions, and/orother relationships between object versions.

The analysis is preferably generated in response to a query (e.g., thequery including an object identifier, geographic coordinates, anaddress, a time, etc.), but can additionally and/or alternatively begenerated in response to a request from an endpoint, be generated beforethe request is received (e.g., when a specific relationship is detected,when new property data is received, etc.), be generated for each change(e.g., calculated when a changed relationship is determined), and/or anyother suitable time. The analysis can be generated by traversing througha graph or a relationship set, by retrieving data associated with anobject version, and/or be otherwise generated.

In an example, S400 can include: receiving a request identifying anobject version; identifying the relationship set (e.g., graph orsubgraph) associated with the object version; and returning informationextracted from measurements associated with (e.g., used to generate) theobject representations within the relationship set (e.g., example shownin FIG. 14 ). In a first illustrative example, S400 can include:receiving a request with an address and a timestamp (e.g., the time atwhich the property was insured); identifying the relationship setassociated with the address (e.g., the geographic region's relationshipset); identifying the object version associated with the timestamp(e.g., the object version with the timeframe encompassing thetimestamp); and generating a response based on the measurementsassociated with the object version (e.g., used to generate the objectrepresentations associated with the object version).

In a second example, S400 can include: receiving the request identifyingthe object version and a set of requested data types, and retrievingobject data associated with the object version that satisfies therequest. However, the analysis can be otherwise initiated and/or used.

Examples of analyses that can be determined include: the relationshipbetween a first and second object version (e.g., the next objectversion, a prior object version, the current object version etc.); atimeseries of changes between a first and second object version (e.g.,change type, change time, building development history, parceldevelopment history, etc.); whether the requested object version is themost recent object version for the geographic region; the most recentobject version for a requested geographic region; the timeframe orduration for a requested object version; data associated with arequested object version (e.g., measurements, object representations,feature vectors, attribute values, etc.); an analysis of the timeseriesthat the requested object version is a part of (e.g., number of changes,types of changes, average object version duration, etc.); an analysis ofthe object versions associated with a requested timeframe; an analysisof the auxiliary data associated with the object version (e.g.,statistical analysis, lookup, prediction, etc.); anomaly detection(e.g., a temporary structure); and/or other analyses (e.g., examplesshown in FIG. 4 ).

In a first example, S400 can include identifying measurements for anobject version and generating the analysis from the set of identifiedmeasurements. The identified measurements are preferably associated withthe object version's timeframe (e.g., sampled during object version'stimeframe; etc.) and/or be otherwise associated with the object version.In a first embodiment, the analysis can include selecting the bestmeasurement (e.g., image) of the object version based on a set ofcriteria (e.g., measurement with minimal shadows, measurement withmaximal resolution, most up-to-date measurement, and least occluded,etc.). In a second embodiment, the analysis can include generating asynthetic measurement from said measurements (e.g., averaged measurementacross a sliding window of measurements, etc.). In a third embodiment,attribute values (e.g., roof slope, etc.) can be extracted from theidentified measurements (and/or best measurement) and be used as theattribute values for the object version. In a fourth embodiment, objectpolygons for the object version can be extracted from the identifiedmeasurements.

In a second example, S400 can include limiting returned data to data forthe specified object variant and/or modifications thereof. For example,if the structure of the specified object (e.g., identified by atimestamp, an object identifier, etc.) was wholly replaced by areplacement structure, information for the original structure can bereturned, and information for the replacement structure would not bereturned.

In a third example, S400 can include generating a timeseries of changesfor a parcel. This can include compiling the time series ofrelationships between serial representations, serial object versions, orbe otherwise performed.

In a fourth example, S400 can include determining whether an objectversion has changed since a reference time or reference object variant.In this variant, the relationship set can be analyzed to determinewhether any change-associated relationships appear after the referencetime or reference object variant.

In a fifth example, S400 can include generating an analysis based onrelationships between object variants within geospatial extent (e.g., aset of geographic coordinates of a neighborhood) and/or a set of objectidentifiers (e.g., a set of addresses). In a first example, the analysiscan include neighborhood-level statistics on object development (e.g.,percentage of objects undergoing development within a neighborhood,average percentage of objects undergoing development across a set ofneighborhoods). In a second example, the analysis can include statisticsfor a list of addresses (e.g., average frequency of change occurrencebetween the addresses within a time interval).

In a sixth example, S400 includes determining whether an object haschanged based on the latest representation and a new representationdetermined from new property data. When the object has changed (e.g.,the relationship between the latest and new representations isindicative of change), the new representation can be stored as thelatest representation for the object (and/or a new object version can becreated), and the prior representation can optionally be discarded;alternatively, the representations can be otherwise handled. Objectinformation extracted from the new property data can optionally bestored in association with the new object version, and the old objectinformation can optionally be discarded or associated with the priorobject version. When the object has not changed (e.g., the relationshipbetween the latest and new representations is not indicative of change),the object information extracted from the new property data can be:discarded, merged with the prior object information, and/or otherwiseused.

However, any other analysis can be performed.

S400 can additionally include providing the analysis to an endpoint(e.g., an endpoint on a network, customer endpoint, user endpoint,automated valuation model system, etc.) through an interface, report, orother medium. The interface can be a mobile application, webapplication, desktop application, an API, and/or any other suitableinterface executing on a user device, gateway, and/or any othercomputing system.

However, S400 can be otherwise performed.

5. Use Cases

The relationship set can be used to track changes of an object over time(e.g., structure modified, structure replaced, structure added,structure removed, auxiliary structure added, auxiliary structuredremoved, actively under construction, etc.), be used to associatemeasurements of the same object instance from different modalities orvendors together (e.g., example shown in FIG. 11 ), and/or otherwiseused. The relationship set can be used for: insurance underwriting(e.g., verify that the originally-insured property still exists or isunchanged; determine pricing of insurance depending on the change;optimize inspection to identify where to send inspectors; determine whento reach out to adjust insurance policy when remodeling is detected;identify which properties to initiate claims for; create opportunitiesto proactively address property change issues before they result ininsurance claims; reconcile object versions with those in their recordsor those that were insured; etc.), real estate property investing (e.g.,identify underpriced properties that can increase in value throughrenovation and/or repairs; incorporate the change into a valuation modelto establish the offer price; determine when construction, remodeling,and/or repairing has occurred; identify properties in portfolio thathave suffered damage; etc.), real estate management (e.g., identifyareas that can be renovated, repaired, added, and/or removed, etc.),real estate valuations (e.g., use change as an input to an automatedvaluation model; use change to detect error in property evaluationmodels; use change as a supplement to a property-level valuation report;etc.), real estate and loan trading (e.g., detect illegal builds;identify deterioration since prior due diligence was completed;incorporate the change into collateral valuation in mortgage originationand in secondary mortgage market; etc.), and/or otherwise used. In anillustrative example, an insurer can use the relationship set todetermine whether a multi-structure campus under development, such as anapartment complex, is under-insured due to recent construction or due toother discrepancies.

However, the relationship set can be otherwise used.

Different processes and/or elements discussed above can be performed andcontrolled by the same or different entities. In the latter variants,different subsystems can communicate via: APIs (e.g., using API requestsand responses, API keys, etc.), requests, and/or other communicationchannels. Communications between systems can be encrypted (e.g., usingsymmetric or asymmetric keys), signed, and/or otherwise authenticated orauthorized.

All references cited herein are incorporated by reference in theirentirety, except to the extent that the incorporated material isinconsistent with the express disclosure herein, in which case thelanguage in this disclosure controls.

Alternative embodiments implement the above methods and/or processingmodules in non-transitory computer-readable media, storingcomputer-readable instructions that, when executed by a processingsystem, cause the processing system to perform the method(s) discussedherein. The instructions can be executed by computer-executablecomponents integrated with the computer-readable medium and/orprocessing system. The computer-readable medium may include any suitablecomputer readable media such as RAMs, ROMs, flash memory, EEPROMs,optical devices (CD or DVD), hard drives, floppy drives, non-transitorycomputer readable media, or any suitable device. The computer-executablecomponent can include a computing system and/or processing system (e.g.,including one or more collocated or distributed, remote or localprocessors) connected to the non-transitory computer-readable medium,such as CPUs, GPUs, TPUS, microprocessors, or ASICs, but theinstructions can alternatively or additionally be executed by anysuitable dedicated hardware device.

Embodiments of the system and/or method can include every combinationand permutation of the various system components and the various methodprocesses, wherein one or more instances of the method and/or processesdescribed herein can be performed asynchronously (e.g., sequentially),contemporaneously (e.g., concurrently, in parallel, etc.), or in anyother suitable order by and/or using one or more instances of thesystems, elements, and/or entities described herein. Components and/orprocesses of the following system and/or method can be used with, inaddition to, in lieu of, or otherwise integrated with all or a portionof the systems and/or methods disclosed in the applications mentionedabove, each of which are incorporated in their entirety by thisreference.

As a person skilled in the art will recognize from the previous detaileddescription and from the figures and claims, modifications and changescan be made to the preferred embodiments of the invention withoutdeparting from the scope of this invention defined in the followingclaims.

We claim:
 1. A method, comprising: detecting a building within a set ofmeasurements using an object detector; determining building parametersbased on the building detection; and determining a building segment forthe building, based on the building component parameters, using asegmentation model.
 2. The method of claim 1, wherein determining thebuilding segment comprises: retrieving a higher resolution image basedon the building parameters; and segmenting the segment from the higherresolution image using the segmentation model.
 3. The method of claim 1,wherein the building parameters comprise a geographic extent, whereinthe higher resolution image depicts the geographic extent.
 4. The methodof claim 1, wherein the measurement and the higher resolutionmeasurement both comprise RGB images.
 5. The method of claim 1, furthercomprising determining a property attribute from the building segment.6. The method of claim 1, wherein detecting the building comprisesdetecting a roof.
 7. The method of claim 1, wherein the propertyattribute comprises at least one of: roof condition, number of rooffacets, roof surface area, or roof slope.
 8. The method of claim 1,further comprising determining a property attribute from the buildingdetection.
 9. A method, comprising: determining a bounding boxsurrounding a building component within a measurement, using a buildingcomponent object detector; determining building component parametersbased on the bounding box; retrieving a higher resolution measurementusing the building component parameters; and determining a set ofbuilding component attributes from the higher resolution measurement.10. The method of claim 9, wherein the measurement comprises a wide-areaimage.
 11. The method of claim 9, wherein the higher resolutionmeasurement depicts a geographic region smaller than the wide-areaimage.
 12. The method of claim 9, wherein the higher resolutionmeasurement comprises depth information.
 13. The method of claim 9,wherein the measurement comprises a RGB image.
 14. The method of claim9, wherein the building component comprises at least one of a roof,solar panel, chimney, HVAC unit, or door.
 15. The method of claim 9,wherein the building component parameters comprise a geolocation of thebuilding component, a dimension of the building component, and anorientation of the building component, determined based on thegeolocation, dimension, and orientation of the bounding box,respectively.
 16. The method of claim 9, wherein the set of buildingcomponent attributes are extracted from the higher resolutionmeasurement using a set of attribute-specific machine learning models.17. The method of claim 9, wherein the object detector is trained on aset of bounding boxes drawn around building segments identified withintraining images, wherein the building segments are determined using asegmentation model.
 18. The method of claim 9, further comprisingdetermining a count of building components based on a count of boundingboxes determined from the measurement.
 19. The method of claim 9,further comprising determining a height parameter of the building basedon the building component parameters and a geolocation of themeasurement.
 20. The method of claim 9, wherein the building componentcomprises a shadow, wherein the height parameter is determined based ona length of the detected shadow, the geolocation of the measurement, anda sampling time for the measurement.